Abstract: In the context of rapid advancements in science and technology, the application scope of IoT technology continues to expand, and its presence can be seen across all industries. The Internet of Things represents China’s third information revolution. With the advent of IoT, every object in the world is interconnected, enabling seamless communication and connectivity among people, machines, and objects anytime and anywhere. Compared with previous network technologies, real-time information exchange based on IoT demonstrates higher efficiency and superior quality. This paper focuses on researching real-time information exchange strategies for big data volumes based on IoT, a study that holds significant research value.

Preface

The Internet of Things (IoT), at its core, is about the exchange and sharing of information—a network connecting things to one another. The emergence of IoT technology has linked objects with objects and objects with people into a visualized network. In this network, it becomes possible to identify, locate, query, track, and monitor objects, thereby enabling efficient, comprehensive, and intelligent management of these objects. IoT technology has demonstrated remarkable performance in real-time data exchange and has been widely adopted across various industries, earning widespread social recognition. However, in the era of big data, where the volume of processed and exchanged information continues to grow exponentially, challenges related to real-time data exchange and access have inevitably surfaced, significantly complicating IoT data exchange processes. To optimize the quality and efficiency of big-data-driven information exchange within the IoT framework, this paper explores strategies for real-time big-data exchange based on IoT technologies. The goal is to enhance the quality of IoT data processing and provide valuable references for expanding the scope of IoT applications across diverse sectors of society.

I. The Necessity of Real-Time Information Exchange with Large Data Volumes

(1) Meet the requirements for efficient processing of data information

To date, the IoT has achieved a remarkably high level of market penetration and adoption, thanks to its outstanding performance in real-time information exchange. The applications of IoT are becoming increasingly widespread; we can already see its presence in various sectors that people are familiar with, such as industry, agriculture, transportation, government administration, education, and healthcare. As a result, the volume of data that needs to be processed for IoT-based information exchange has grown significantly, leading to a further decline in the density of information distribution and, consequently, a substantial reduction in the efficiency and quality of information exchange. Therefore, it is essential to conduct an in-depth study of the demand for rapid processing of information data in the IoT-based information exchange process. At the heart of information exchange lies the mutual exchange and communication of IoT data and information resources, which requires connecting computer networks with physical objects via data channels and then processing and integrating the resulting information data. To ensure both the speed and quality of information data exchange, it is necessary first to perform a series of basic preprocessing steps on the data—such as compressing information resources and converting data formats—before proceeding with real-time information exchange to meet the need for rapid data transmission.

(2) Meet the requirements for data and information sharing

The primary purpose of the Internet of Things is to optimize the utilization of data resources, enabling real-time sharing of information and data among various interconnected yet distinct entities, thereby driving economic development. In the process of real-time exchange of IoT data and information resources, special attention should be paid to the consistency of data formats and the compatibility among data modules—these are the foundation and prerequisite for real-time information exchange involving large volumes of data. Only by maintaining consistent data formats and ensuring mutual compatibility among data modules can we achieve true data sharing. However, at present, the compatibility among IoT data modules still needs to be improved; there are shortcomings in data standardization, and the timeliness of large-volume information resources during sharing is low, which hinders the efficient circulation of information and urgently calls for a solution to this issue.

(3) Meet the requirements for secure data transmission

Against the backdrop of the ongoing advancement in real-time information sharing of massive IoT data, the volume of data resources continues to grow exponentially. In the process of data exchange, it is inevitable that issues such as leakage, loss, and tampering may arise. To ensure the security and comprehensiveness of real-time information exchange, it is essential first to address problems like information resource leakage and loss. Only by safeguarding the security and integrity of information resources can we achieve efficient and high-quality exchange of massive IoT data.

II. The Issue of Real-Time Information Exchange with Large Volumes of Big Data Based on the Internet of Things

(1) Data information was not handled in a timely manner.

The process of real-time information exchange involving large volumes of data is highly cumbersome and involves complex procedures. In actual operations, it requires continuous read and write operations on the data, which in turn leads to an increased number of database accesses and more frequent access requests. This, in turn, affects the timely entry of subsequent data into the database, making it impossible to process newly entered data quickly and efficiently, thereby directly impacting the user experience. Particularly when dealing with critical data resources, if the aforementioned situation occurs, it could lead to serious adverse consequences.

(2) The system processing tools are not functioning properly.

Against the backdrop of the rapid development of the internet, the volume of information data generated by social, production, and daily life activities has surged dramatically, placing increasingly stringent demands on real-time information processing in the Internet of Things (IoT). As a result, the amount of information processed through IoT systems has grown even larger. The excessively rapid growth in data volume has led to a sharp increase in the pressure placed on databases. If a database suffers from insufficient storage space, this will inevitably impair its access efficiency, slowing down access speeds and negatively affecting the efficiency and timeliness of information transmission. Moreover, it can also hinder the normal operation of system processing tools, thereby impeding the comprehensive advancement of IoT development.

(3) The query function responds slowly.

Against the backdrop of continuously growing data volumes, the contradiction between the limited capacity of IoT systems and the virtually unlimited amount of data is becoming increasingly apparent. With massive amounts of information stored in the system, if this data is not effectively processed or adequately planned for, it will inevitably lead to data exceeding the system’s capacity, further degrading system performance. The influx of massive data into a limited system results in slower query response times, directly impacting the timeliness of information exchange and making it impossible to achieve timely and effective data processing.

III. Specific Strategies for Real-Time Information Exchange Based on Big Data in the IoT Context

(1) Temporary Table Technology

Real-time information exchange involving large volumes of big data, underpinned by the Internet of Things, requires thorough data preprocessing. Traditionally, data table processing has relied on specific data-table-joining methods to compute data tables and then filter out data that meets predefined criteria from these computational results. However, this approach suffers from a significant drawback: the directly computed tables often lack completeness and stability, which in turn leads to relatively low efficiency in their use. Temporary-table computation differs markedly from conventional data-table-processing methods. Its core principle is to first select data that meets the criteria from large data tables and store it in temporary data tables, and then process the data in these temporary tables. By leveraging temporary-table technology, information processing and exchange become faster and more efficient. Compared to accessing the original source data, accessing data in temporary tables offers advantages such as higher speed, greater efficiency, and enhanced accuracy, thereby effectively improving the timeliness of information data processing. This technique is particularly suitable for scenarios where large datasets are frequently read and the types of data being accessed are relatively concentrated and fixed, as it can optimize system performance and enhance the quality of real-time information exchange.

(2) Memory-mapped file method

Real-time information exchange involving large volumes of IoT data is typically carried out on computer hard drives. As the volume of data continues to grow, the amount of data that needs to be processed has increased dramatically, making data processing increasingly challenging. Under these circumstances, adopting the original operational methods alone can no longer achieve the desired level of processing efficiency and speed. Against this backdrop, the memory-mapped file approach has gradually gained widespread adoption. This method boasts higher processing efficiency and more sophisticated operational techniques, making it well-suited for handling large-volume files. The core principle behind the memory-mapped file approach lies in leveraging memory pointers to enable applications to access files stored on disk as if they were directly accessing memory that has been loaded with those files. By means of file mapping, the corresponding content within a disk file is mapped onto a specific range of the process's virtual address space. In traditional operations, file I/O operations and file-content buffering are required; however, in the memory-mapped file approach, these steps and processes are entirely eliminated. When using the memory-mapped file method to process files, there is no need to allocate or reserve buffers for files—instead, the system directly handles all operations related to file caching. This approach allows for highly efficient access to the mapped files, effectively conserves memory space, and significantly enhances system performance. Specifically for real-time information exchange involving large volumes of IoT data, the application of this method eliminates the need for operations such as loading file data into memory, writing data back from memory to files, and releasing memory blocks. Instead, only four steps are required: creating a file-mapping object, establishing a mapping object and view of the mapped file within the process's address space, unmapping the file after data processing is complete, and closing the file-mapping object. These steps greatly improve data-processing efficiency. This data-storage and access method holds broad potential for application in real-time information exchange and sharing involving large volumes of IoT data. In the process of implementing the memory-mapped file approach, there are three key elements, which are described below:

(1) Configure a specific memory pointer to the computer’s hard disk so that users can more intuitively perceive and understand it during actual use.

(2) Set access permissions and perform identity authentication to prevent information leakage, loss, or tampering, thereby enhancing data security.

(3) Build an information mapping system that can achieve rapid storage, retrieval, and sharing of information. Through this efficient access method, the speed of information updates will be accelerated, and the storage capacity of the resource library will be expanded.

(3) File Buffering Strategy

Database data buffering (I/O buffering) strategies are typically categorized into two types: table-buffering mode and row-buffering mode. Table-buffering mode is characterized by its one-time processing nature, whereas row-buffering mode offers real-time processing and represents a more standard approach for handling data in real time. Each of these operational modes has its own set of limitations. As data volumes grow explosively, adopting the table-buffering mode can lead to substantial occupation of computer storage space. If the computer’s storage capacity falls short of the amount of data stored in the buffer, it can impede the normal operation of the computer system, slow down overall performance, and make it increasingly difficult to meet the demands of efficient data processing. In scenarios involving large database logs, if row-buffering mode is employed, the efficiency of data reading and transmission tends to be low; it takes considerably longer to complete data reading and transmission, significantly reducing system performance and severely compromising the timeliness of information exchange. To address the current situation and overcome the shortcomings inherent in traditional database data-buffering strategies, it is necessary to adopt an optimized file-buffering strategy. For real-time information exchange involving massive data volumes, the file-buffering strategy can be used effectively: data logs can be downloaded to temporary files on the local client, after which the data log files can be processed efficiently. By leveraging the file-buffering strategy, we can effectively resolve issues such as insufficient memory space or low information-exchange efficiency, further enhancing system performance and optimizing the overall system effectiveness. When performing complex operations on databases—such as summation operations—due to the specific management mechanisms within the database itself, the advantages of the file-buffering strategy cannot be fully realized. However, when multiple reads of the same large-capacity database are required, especially in cases where multi-level association rules for data mining need to be obtained, the file-buffering strategy proves to be a relatively effective approach.

(4) Query Exchange Optimization Method

In the Internet of Things, real-time information exchange involves large volumes of big data, and users primarily focus on querying and accessing such data. The methods for optimizing query and access can be broadly categorized into two main types: cache technology and indexing technology.

Regarding caching technology, access frequencies are relatively high, and changes in data information over short periods are difficult to detect. Moreover, the overall volume of information grows at a relatively modest rate. The greatest advantage of this technology lies in its ability to significantly enhance data query efficiency, enabling multiple, repeated, and high-frequency accesses to large volumes of information resources within extremely short timeframes. At the same time, the quality of real-time data exchange can be effectively improved. By applying caching technology, many redundant and invalid data entries can be automatically and efficiently filtered out, eliminating the problems associated with repeated data transmission, processing, and creation. As a result, system performance is markedly enhanced, system response speeds are greatly accelerated, and the frequency of digital library accesses is reduced, thereby improving both the quality and efficiency of information exchange services. Additionally, this approach helps prevent sudden service interruptions during data provision, ensuring the stability and availability of the system.

With regard to retrieval techniques, we can further classify them into clustered index techniques and non-clustered index techniques. Both approaches have their own advantages and disadvantages. A clustered index enables the complete storage of massive amounts of data in a specific order, allowing it to immediately provide the required information when a user performs an indexing operation. As a result, clustered indexes excel in retrieval efficiency; however, they also have certain limitations, primarily in that newly added data cannot be displayed in real time. Moreover, clustered indexes are prone to affecting data insertion, modification, and deletion operations. In contrast to clustered indexes, non-clustered indexes have lower retrieval efficiency. Nevertheless, they can quickly display newly added data, offer greater completeness of the queried information, and do not adversely affect data insertion, modification, or deletion.

Conclusion

In summary, the development of the Internet of Things (IoT) provides crucial technological support for real-time information exchange involving large volumes of data. The application of IoT technology addresses the shortcomings of traditional data exchange methods—namely, poor timeliness and inadequate security. Based on IoT, real-time information exchange involving large volumes of data boasts advantages such as high efficiency, rapid responsiveness, and enhanced security. Against the backdrop of the big data era, it is essential to further intensify research into real-time information exchange using IoT’s large data volumes. We must also adopt scientific, rational, and targeted solutions to the challenges faced by IoT-based real-time information exchange, thereby providing robust technical support for such exchanges and promoting the stable development of IoT-driven real-time information exchange involving large data volumes.

Due to space limitations, the footnotes have been omitted. For the complete version, please visit ShuiBiao.com for free access.

Source: SanChuan Wisdom

Authors: Zheng Zhiqiang, Tang Xiong

Editor: Li Jingshuai

First Instance: Zhou Qi

Second Instance: Zhan Zhijie, Cai Jinhui